Annual Review of Vision Science - Volume 9, 2023

Augmented reality (AR) systems aim to alter our view of the world and enable us to see things that are not actually there. The resulting discrepancy between perception and reality can create compelling entertainment and can support innovative approaches to education, guidance, and assistive tools. However, building an AR system that effectively integrates with our natural visual experience is hard. AR systems often suffer from visual limitations and artifacts, and addressing these flaws requires basic knowledge of perception. At the same time, AR system development can serve as a catalyst that drives innovative new research in perceptual science. This review describes recent perceptual research pertinent to and driven by modern AR systems, with the goal of highlighting thought-provoking areas of inquiry and open questions.

Functional magnetic resonance imaging (fMRI), the key methodology for mapping the functions of the human brain in a noninvasive manner, is limited by low temporal and spatial resolution. Recent advances in ultra-high field (UHF) fMRI provide a mesoscopic (i.e., submillimeter resolution) tool that allows us to probe laminar and columnar circuits, distinguish bottom-up versus top-down pathways, and map small subcortical areas. We review recent work demonstrating that UHF fMRI provides a robust methodology for imaging the brain across cortical depths and columns that provides insights into the brain's organization and functions at unprecedented spatial resolution, advancing our understanding of the fine-scale computations and interareal communication that support visual cognition.

Deep neural networks (DNNs) are machine learning algorithms that have revolutionized computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to the suggestion that DNNs may also be good models of human visual perception. In this article, we review evidence regarding current DNNs as adequate behavioral models of human core object recognition. To this end, we argue that it is important to distinguish between statistical tools and computational models and to understand model quality as a multidimensional concept in which clarity about modeling goals is key. Reviewing a large number of psychophysical and computational explorations of core object recognition performance in humans and DNNs, we argue that DNNs are highly valuable scientific tools but that, as of today, DNNs should only be regarded as promising—but not yet adequate—computational models of human core object recognition behavior. On the way, we dispel several myths surrounding DNNs in vision science.